Completeness of optional attributes

Characteristic Name:	Completeness of optional attributes
Dimension:	Completeness
Description:	Optional attributes should not contain invalid null values
Granularity:	Element
Implementation Type:	Rule-based approach
Characteristic Type:	Declarative

Verification Metric:

The number of invalid null values reported in an optional attribute per thousand records

GuidelinesExamplesDefinitons

The implementation guidelines are guidelines to follow in regard to the characteristic. The scenarios are examples of the implementation

Guidelines:	Scenario:
Provide default values for each valid case of null values for the attribute in concern so that null values occur only for actually missing values which are invalid cases for the attribute in concern.	Case1: Attribute values that are not defined for a particular entity instance (e.g.: Maiden name of unmarried women ) Such instances will get the default value “NOT DEFINED” Case2 : Attribute values that are defined for the entity instance whereas the real value for the attribute instance is null (eg: Vehicle number of a student who does not have a vehicle) Such instances will get the default value “NOT EXIST” Case3: Attribute values are defined for the entity instance and the attribute instance should have a value (Student’s date of birth).

Guidelines:

Scenario:

Provide default values for each valid case of null values for the attribute in concern so that null values occur only for actually missing values which are invalid cases for the attribute in concern.

Case1: Attribute values that are not defined for a particular entity instance (e.g.: Maiden name of unmarried women ) Such instances will get the default value “NOT DEFINED”

Case2 : Attribute values that are defined for the entity instance whereas the real value for the attribute instance is null (eg: Vehicle number of a student who does not have a vehicle) Such instances will get the default value “NOT EXIST”

Case3: Attribute values are defined for the entity instance and the attribute instance should have a value (Student’s date of birth).

Validation Metric:

How mature is the creation and implementation of the DQ rules to define valid null cases

These are examples of how the characteristic might occur in a database.

Example:	Source:
Let us consider a Person relation with the attributes Name, Surname, BirthDate,and Email. The relation is shown in Figure 2.2. For the tuples with Id equalto2,3,and 4, the Email value is NULL. Let us suppose that the person represented by tuple 2 has no e-mail: no incompleteness case occurs. If the person represented by tuple 3 has an e-mail, but its value is not known then tuple 3 presents an incompleteness. Finally, if it is not known whether the person represented by tuple 4 has an e-mail or not, incompleteness may not be the case. ID 1 2 3 4 Name John Edward Anthony Marianne Surname Smith Monroe White Collins BirthDate 03/17/1974 02/03/1967 01/01/1936 11/20/1955 Email smith@abc.it NULL NULL NULL not existing existing but unknown not known if existing Fig. 2.2. The Person relation, with diﬀerent null value meanings for the e-mail attribute	C. Batini and M, Scannapieco, “Data Quality: Concepts, Methodologies, and Techniques”, Springer, 2006.
1) A database contains information on repairs done to capital equipment. How- ever, it is a known fact that sometimes the repairs are done and the informa- tion about the repair is just not entered into the database. This is the result of lack of concern on the part of the repair people and a lack of enforcement on the part of their supervisors. It is estimated that the amount of missing information is about 5%. This database is probably a good-quality database for assessing the genral health of capital equipment. Equipment that required a great deal of expense to maintain can be identiﬁed from the data. Unless the missing data is disproportionately skewed, the records are usable for all ordinary decisions. However, trying to use it as a base for evaluating information makes it a low-quality database. The missing transactions could easily tag an important piece of equipment as satisfying a warranty when in fact it does not. 2) A blank for COLLEGE_LAST_ATTENDED may be accurate or inaccurate. If the person it applied to had attended college, it would be inaccurate. This is another case of valid but not accurate.	J. E. Olson, “Data Quality: The Accuracy Dimension”, Morgan Kaufmann Publishers, 9 January 2003.

Example:

Source:

Let us consider a Person relation with the attributes Name, Surname,

BirthDate,and Email. The relation is shown in Figure 2.2. For the tuples with Id equalto2,3,and 4, the Email value is NULL. Let us suppose that the person represented by tuple 2 has no e-mail: no incompleteness case occurs. If the person represented by tuple 3 has an e-mail, but its value is not known then tuple 3 presents an incompleteness. Finally, if it is not known whether the person represented by tuple 4 has an e-mail or not, incompleteness may not be the case.

ID 1

2 3 4

Name John

Edward Anthony Marianne

Surname Smith

Monroe White Collins

BirthDate 03/17/1974 02/03/1967 01/01/1936 11/20/1955

smith@abc.it NULL NULL NULL

not existing existing but unknown not known if existing

Fig. 2.2. The Person relation, with diﬀerent null value meanings for the e-mail attribute

C. Batini and M, Scannapieco, “Data Quality: Concepts, Methodologies, and Techniques”, Springer, 2006.

1) A database contains information on repairs done to capital equipment. How- ever, it is a known fact that sometimes the repairs are done and the informa- tion about the repair is just not entered into the database. This is the result of lack of concern on the part of the repair people and a lack of enforcement on the part of their supervisors. It is estimated that the amount of missing information is about 5%. This database is probably a good-quality database for assessing the genral health of capital equipment. Equipment that required a great deal of expense to maintain can be identiﬁed from the data. Unless the missing data is disproportionately skewed, the records are usable for all ordinary decisions. However, trying to use it as a base for evaluating information makes it a low-quality database. The missing transactions could easily tag an important piece of equipment as satisfying a warranty when in fact it does not.

2) A blank for COLLEGE_LAST_ATTENDED may be accurate or inaccurate. If the person it applied to had attended college, it would be inaccurate. This is another case of valid but not accurate.

J. E. Olson, “Data Quality: The Accuracy Dimension”, Morgan Kaufmann Publishers, 9 January 2003.

The Definitions are examples of the characteristic that appear in the sources provided.

Definition:	Source:
1) A null value is a missing value. However, a value that is missing may provide more information than one might think because there may be different reason that it is missing. A null value might actually represent an unavailable value, an attribute that is not applicable for this entity, or no value in the attribute’s domain that correctly classifies this entity. Of course, the value may actually be missing. 2) When the null value (or absence of a value) is required for an attribute, there should be a recognizable form for presenting that null value that does not conflict with any valid values.	LOSHIN, D. 2001. Enterprise knowledge management: The data quality approach, Morgan Kaufmann Pub.
1) Ability to distinguish neatly (without ambiguities) null and default values from applicable values of the domain. 2) Completeness refers to the degree to which values are present in a data collection, as for as an individual datum is concerned, only two situations are possible: Either a value is assigned to the attribute in question or not. In the latter case, null, a special element of an attribute’s domain can be assigned as the attribute’s value. Depending on whether the attribute is mandatory, optional, or inapplicable, null can mean different things.	REDMAN, T. C. 1997. Data quality for the information age, Artech House, Inc.

Definition:

Source:

1) A null value is a missing value. However, a value that is missing may provide more information than one might think because there may be different reason that it is missing. A null value might actually represent an unavailable value, an attribute that is not applicable for this entity, or no value in the attribute’s domain that correctly classifies this entity. Of course, the value may actually be missing.

2) When the null value (or absence of a value) is required for an attribute, there should be a recognizable form for presenting that null value that does not conflict with any valid values.

LOSHIN, D. 2001. Enterprise knowledge management: The data quality approach, Morgan Kaufmann Pub.

1) Ability to distinguish neatly (without ambiguities) null and default values from applicable values of the domain.

2) Completeness refers to the degree to which values are present in a data collection, as for as an individual datum is concerned, only two situations are possible: Either a value is assigned to the attribute in question or not. In the latter case, null, a special element of an attribute’s domain can be assigned as the attribute’s value. Depending on whether the attribute is mandatory, optional, or inapplicable, null can mean different things.

REDMAN, T. C. 1997. Data quality for the information age, Artech House, Inc.

Accuracy to reality

Characteristic Name:	Accuracy to reality
Dimension:	Accuracy
Description:	Data should truly reflect the real world
Granularity:	Record
Implementation Type:	Process-based approach
Characteristic Type:	Usage

Verification Metric:

The number of tasks failed or under performed due to lack of accuracy to reality

The number of complaints received due to lack of accuracy to reality

GuidelinesExamplesDefinitons

The implementation guidelines are guidelines to follow in regard to the characteristic. The scenarios are examples of the implementation

Guidelines:	Scenario:
Continuously evaluate if the existing data model is sufficient to represent the real world as required by the organisational need and do the necessary amendments to the data model if needed.	(1) A student who received a concession travel card is not eligible for a concession fare if he terminates his candidature before completion of the course. Hence the data model should have an extra attribute as "current status of candidature"
Perform regular audits on mission critical data to verify that every record has a meaningful existence in the reality which is useful for the organisation	(1) All customers existing in the customer master file actually a customer in the customer space open for the organisation. (non customers are not in the customer file) (2) "Greg Glass" is recorded as a glass work company but in fact they are opticians (3) A person's personal details taken from his educational profile may not be a correct reality for his insurance profile even though the information is
Perform regular audits on mission critical data to verify that every record has a unique existence in the reality	(1) It is difficult to find out that the professor "Andrew" is from Colombia university or from the university of Queensland
Ensure that Information available in the system is accurate in the context of a particular activity or event	(1) The driver details taken from vehicle registration may not be accurate in the case of finding the real person who drive the vehicle when an accident is caused

Validation Metric:

How mature is the process to ensure the accuracy to reality

These are examples of how the characteristic might occur in a database.

Example:	Source:
if the name of a person is John, the value v = John is correct, while the value v = Jhn is incorrect	C. Batini and M, Scannapieco, “Data Quality: Concepts, Methodologies, and Techniques”, Springer, 2006.
Percent of values that are correct when compared to the actual value. For example, M=Male when the subject is Male.	P. Cykana, A. Paul, and M. Stern, “DoD Guidelines on Data Quality Management” in MIT Conference on Information Quality - IQ, 1996, pp. 154-171.
an EMPLOYEE entity (identified by the Employee-Number 314159) and the attribute Year-of-Birth. If the value of Year-of-Birth for employee 314159 is the year the employee was born, the datum is correct.	C. Fox, A. Levitin, and T. Redman, “The Notion of Data and Its Quality Dimensions” in Journal Information Processing and Management: an International Journal archive, Volume 30 Issue 1, Jan-Feb 1994, 1992, pp. 9-19.
Consider a database that contains names, addresses, phone numbers, and e- mail addresses of physicians in the state of Texas. This database is known to have a number of errors: some records are wrong, some are missing, and some are obsolete. If you compare the database to the true population of physicians, it is expected to be 85% accurate. If this database is to be used for the state of Texas to notify physicians of a new law regarding assisted suicide, it would certainly be considered poor quality. In fact, it would be dangerous to use it for that intended purpose. 24 2.1 Data Quality Definitions 25 If this database were to be used by a new surgical device manufacturer to ﬁnd potential customers, it would be considered high quality. Any such ﬁrm would be delighted to have a potential customer database that is 85% accurate. From it, they could conduct a telemarketing campaign to identify real sales leads with a completely acceptable success rate. The same database: for one use it has poor data quality, and for another it has high data quality.	J. E. Olson, “Data Quality: The Accuracy Dimension”, Morgan Kaufmann Publishers, 9 January 2003.
The patient’s identification details are correct and uniquely identify the patient.	P. J. Watson, “Improving Data Quality: A Guide for Developing Countries”, World Health Organization, 2003.

The Definitions are examples of the characteristic that appear in the sources provided.

Definition:	Source:
Determines the extent to which data objects correctly represent the real-world values for which they were designed. For example, the sales orders for the Northeast region must be assigned a Northeast sales representative.	D. McGilvray, “Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information”, Morgan Kaufmann Publishers, 2008.
The data value correctly reflects the real-world condition.	B. BYRNE, J. K., D. MCCARTY, G. SAUTER, H. SMITH, P WORCESTER 2008. The information perspective of SOA design Part 6:The value of applying the data quality analysis pattern in SOA. IBM corporation.
The data correctly reflects the Characteristics of a Real-World Object or Event being described. Accuracy and Precision represent the highest degree of inherent Information Quality possible.	ENGLISH, L. P. 2009. Information quality applied: Best practices for improving business information, processes and systems, Wiley Publishing.
Is the information precise enough and close enough to reality?	EPPLER, M. J. 2006. Managing information quality: increasing the value of information in knowledge-intensive products and processes, Springer.
1) Each identifiable data unit maps to the correct real-world phenomenon. 2) Non-identifying (i.e. non-key) attribute values in an identifiable data unit match the property values for the represented real-world phenomenon. 3) Each identifiable data unit represents at least one specific real-world phenomenon. 4) Each identifiable data unit represents at most one specific real-world phenomenon.	PRICE, R. J. & SHANKS, G. Empirical refinement of a semiotic information quality framework. System Sciences, 2005. HICSS'05. Proceedings of the 38th Annual Hawaii International Conference on, 2005. IEEE, 216a-216a.
1) The degree to which an information object correctly represents another information object, process, or phenomenon in the context of a particular activity or culture. 2) Closeness of agreement between a property value and the true value (value that characterizes a characteristic perfectly defined in the conditions that exists when the characteristic is considered. 3) The extent to which the correctness of information is verifiable or provable in the context of a particular activity.	STVILIA, B., GASSER, L., TWIDALE, M. B. & SMITH, L. C. 2007. A framework for information quality assessment. Journal of the American Society for Information Science and Technology, 58, 1720-1733.